Comparison of Community Practitioner and Clinical Educator Expectations of Veterinary Graduates.

One goal of veterinary curricular development and revision is to ensure graduating veterinarians meet entry-level competencies to perform successfully in their community. Most curricula are developed by clinical educators in a university setting; therefore, we must determine whether clinical educators can predict community practitioner expectations. This article evaluates practitioners' expectations of new graduate independence in veterinary tasks and compares these expectations with those of clinical educators at the University of Wisconsin-Madison School of Veterinary Medicine (UW-SVM). A survey was designed to measure expectations of graduate-level independence within nine technical and three non-technical categories. Members of the Wisconsin Veterinary Medical Association (WVMA) and UW-SVM clinicians were invited to participate. Expected levels of independence were compared between these two populations and between WVMA specialists and generalists. Results indicated significant differences in the expected levels of graduate independence between UW-SVM clinicians and WVMA members, with UW-SVM clinicians generally expecting higher levels of independence for both technical and non-technical tasks. Although most SVM clinicians are specialists, this difference does not appear to reflect a difference in expectations between specialists and generalists, as WVMA specialists had lower expectations of graduate independence for most technical and non-technical tasks than did WVMA generalists. These results suggest that academic clinicians are not able to predict practitioners' graduate expectations or that graduates in practice are not meeting the levels of independence expected by their clinical educators. Further investigation into the differences in expectations will enable fruitful partnerships between academic clinicians, practitioners, and students in curricular design and revision.

Hot topic: Detecting digital dermatitis with computer vision.

Digital dermatitis (DD) is linked to severe lameness, infertility, and decreased milk production in cattle. Early detection of DD provides an improved prognosis for treatment and recovery; however, this is extremely challenging on commercial dairy farms. Computer vision (COMV) models can help facilitate early DD detection on commercial dairy farms. The aim of this study was to develop and implement a novel COMV tool to identify DD lesions on a commercial dairy farm. Using a database of more than 3,500 DD lesion images, a model was trained using the YOLOv2 architecture to detect the M-stages of DD. The YOLOv2 COMV model detected DD with an accuracy of 71%, and the agreement was quantified as "moderate" by Cohen's kappa when compared with a human evaluator for the internal validation. In the external validation, the YOLOv2 COMV model detected DD with an accuracy of 88% and agreement was quantified as "fair" by Cohen's kappa. Implementation of COMV tools for DD detection provides an opportunity to identify cows for DD treatment, which has the potential to lower DD prevalence and improve animal welfare on commercial dairy farms.

Bayesian and Machine Learning Models for Genomic Prediction of Anterior Cruciate Ligament Rupture in the Canine Model.

Anterior cruciate ligament (ACL) rupture is a common, debilitating condition that leads to early-onset osteoarthritis and reduced quality of human life. ACL rupture is a complex disease with both genetic and environmental risk factors. Characterizing the genetic basis of ACL rupture would provide the ability to identify individuals that have high genetic risk and allow the opportunity for preventative management. Spontaneous ACL rupture is also common in dogs and shows a similar clinical presentation and progression. Thus, the dog has emerged as an excellent genomic model for human ACL rupture. Genome-wide association studies (GWAS) in the dog have identified a number of candidate genetic variants, but research in genomic prediction has been limited. In this analysis, we explore several Bayesian and machine learning models for genomic prediction of ACL rupture in the Labrador Retriever dog. Our work demonstrates the feasibility of predicting ACL rupture from SNPs in the Labrador Retriever model with and without consideration of non-genetic risk factors. Genomic prediction including non-genetic risk factors approached clinical relevance using multiple linear Bayesian and non-linear models. This analysis represents the first steps toward development of a predictive algorithm for ACL rupture in the Labrador Retriever model. Future work may extend this algorithm to other high-risk breeds of dog. The ability to accurately predict individual dogs at high risk for ACL rupture would identify candidates for clinical trials that would benefit both veterinary and human medicine.

Machine learning for syndromic surveillance using veterinary necropsy reports.

The use of natural language data for animal population surveillance represents a valuable opportunity to gather information about potential disease outbreaks, emerging zoonotic diseases, or bioterrorism threats. In this study, we evaluate machine learning methods for conducting syndromic surveillance using free-text veterinary necropsy reports. We train a system to detect if a necropsy report from the Wisconsin Veterinary Diagnostic Laboratory contains evidence of gastrointestinal, respiratory, or urinary pathology. We evaluate the performance of several machine learning algorithms including deep learning with a long short-term memory network. Although no single algorithm was superior, random forest using feature vectors of TF-IDF statistics ranked among the top-performing models with F1 scores of 0.923 (gastrointestinal), 0.960 (respiratory), and 0.888 (urinary). This model was applied to over 33,000 necropsy reports and was used to describe temporal and spatial features of diseases within a 14-year period, exposing epidemiological trends and detecting a potential focus of gastrointestinal disease from a single submitting producer in the fall of 2016.